dp_multiparty_data_release

Official code of Differentially Private Multi-Party Data Release for Linear Regression in UAI 2022 by Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q Weinberger, Chong Wang

0. Data Preparation

Download the data folder from here and the structure is

./data/
    UCI/
        bike_sharing/processed_data.ptl
        gpu/processed_data.ptl
        insurance/processed_data.ptl
        superconduct/processed_data.ptl
        year_prediction/processed_data.ptl

1. Run the Experiment with Synthetic Data

Run the following script for seed=0, ..., 999

python private_linear_regression_synthetic_multiparty.py --seed $seed --seed_num 1

To get the Figure 2, check the Jupyter notebook Results-Synthetic.ipynb.

2. Run the Experiment with Real-World Data

Run the following script for dataset in {bike_sharing, gpu, insurance, superconduct, year_prediction} and eps in {0.1, 0.3, 1.0}:

python private_linear_regression_realworld_multiparty.py --dataset $dataset --seed 0 --eps $eps

To Get the Table 1, check the Jupyter notebook Results-RealWorld.ipynb.